{"id":"W2045967366","doi":"10.1371/journal.pone.0041283","title":"Metagenomic Annotation Networks: Construction and Applications","year":2012,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Metagenomics; Annotation; Computer science; Hierarchical organization; Data science; Variety (cybernetics); Computational biology; Biology; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001030113,0.00006489619,0.00007262897,0.00001657513,0.00006771965,0.00001593463,0.00004001283,0.00007592283,0.000009993657],"category_scores_gemma":[0.000005038927,0.00006633475,0.00001733942,0.00003737425,0.00004608773,0.00000603855,0.00003615209,0.00004683972,0.00001175795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005625525,"about_ca_system_score_gemma":0.0000075117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001303673,"about_ca_topic_score_gemma":0.000001429941,"domain_scores_codex":[0.999598,0.00001215138,0.000116641,0.00008616399,0.00004426876,0.0001427242],"domain_scores_gemma":[0.9997078,0.000005211893,0.00005713868,0.0001361078,0.00002822282,0.00006554966],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001238874,0.001286894,0.05293226,0.0002422726,0.00143596,2.117785e-7,0.0003991846,0.0006425797,0.7359682,0.01884284,0.002063221,0.1860625],"study_design_scores_gemma":[0.007375664,0.001468263,0.0807772,0.0002606317,0.003250131,0.0002133738,0.002175159,0.05893266,0.5247056,0.007952751,0.3081388,0.004749728],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8332247,0.00569173,0.1567133,0.00009519325,0.00008127331,0.0005233969,0.00001589154,0.00002458516,0.003629885],"genre_scores_gemma":[0.9835628,0.0005995576,0.01461495,0.0001654694,0.0006980191,0.00005612442,0.0001409892,0.00001001737,0.0001520434],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3060756,"threshold_uncertainty_score":0.2705052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01501157600094982,"score_gpt":0.2032864267343791,"score_spread":0.1882748507334293,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}